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SODB facilitates comprehensive exploration of spatial omics data

Nature Methods volume 20, pages 387–399 (2023)Cite this article

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A Publisher Correction to this article was published on 17 March 2023

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Abstract

Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/. The command-line package is available at https://pysodb.readthedocs.io/en/latest/.

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Fig. 1: Overview.
Fig. 2: Data characteristics and statistics.
Fig. 3: Interactive views of SODB.
Fig. 4: SOView demonstration of various spatial omics datasets.
Fig. 5: SOView discovers both known and unknown functional tissue domains.
Fig. 6: SODB advances computational methods development.

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Data availability

All the primary links of raw data are provided on the web page of datasets. All processed data can be downloaded via the SODB website (https://gene.ai.tencent.com/SpatialOmics/) or pysodb package (https://pysodb.readthedocs.io/en/latest/).

Code availability

The SODB website is available at https://gene.ai.tencent.com/SpatialOmics/. Code for the SODB project is available at https://github.com/yuanzhiyuan/SODB_analysis/. Code for pysodb is available at https://github.com/TencentAILabHealthcare/pysodb. Please refer to Supplementary Table 9 for detailed information on code and resources.

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Acknowledgements

Z.Y. acknowledges the support from the Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01), ZJ Laboratory, Shanghai Center for Brain Science and Brain-Inspired Technology and 111 Project (no. B18015). M.Q.Z. acknowledges support by the Cecil H. and Ida Green Distinguished Chair. We thank L. Wang of Tencent for technical support.

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Author notes

  1. These authors contributed equally: Zhiyuan Yuan, Wentao Pan.

Authors and Affiliations

  1. Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Zhiyuan Yuan

  2. Tencent AI Lab, Shenzhen, China

    Zhiyuan Yuan, Wentao Pan, Xuan Zhao, Zhimeng Xu & Jianhua Yao

  3. Shenzhen International Graduate School, Tsinghua University, Shenzen, China

    Wentao Pan & Xiu Li

  4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Fangyuan Zhao & Yi Zhao

  5. University of Chinese Academy of Sciences, Beijing, China

    Fangyuan Zhao & Yi Zhao

  6. Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA

    Michael Q. Zhang

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Contributions

J.Y., Z.Y. and M.Q.Z. designed the project. Z.Y. performed data collection. Website design was by Z.Y. and X.Z. J.Y., X.L. and Y.Z. provided technical support. Biological interpretation was by M.Q.Z. and Y.Z. Data statistics were performed by Z.Y. Website implementation was by X.Z. and W.P. Figure generation was by Z.Y. and F.Z. Z.Y. and W.P. wrote the manuscript. Z.X. maintains the website. J.Y. and M.Q.Z. reviewed the manuscript. All authors approved the final manuscript.

Corresponding authors

Correspondence to Zhiyuan Yuan, Michael Q. Zhang or Jianhua Yao.

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Nature Methods thanks the anonymous reviewers for their contributions to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–42, Notes 1 and 2 and Tables 7–10.

Reporting Summary

Supplementary Table 1

Experiment information of SODB.

Supplementary Table 2

Dataset information of SODB.

Supplementary Table 3

Biotechnology information of SODB.

Supplementary Table 4

Review article containing computational methods.

Supplementary Table 5

Computational methods and their categories.

Supplementary Table 6

Datasets of SODB used by computational methods.

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Yuan, Z., Pan, W., Zhao, X. et al. SODB facilitates comprehensive exploration of spatial omics data. Nat Methods 20, 387–399 (2023). https://doi.org/10.1038/s41592-023-01773-7

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